DatriseAI-first ETL

Pocket GoodData

AI-first ETL from Pocket into GoodData. Governed entities, incremental sync, typed landing tables.

How Datrise loads Pocket into GoodData

Datrise syncs Pocket's records, events, and configuration objects into GoodData as warehouse tables GoodData maps into its logical data model. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date dimensions.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. GoodData's LDM maps datasets by keys, so Datrise lands stable primary and foreign id columns to keep the model valid.

Ideal for embedded, multi-tenant analytics.

Endpoints

Pocket: SaaS or API data source for analytics and warehouse sync.

GoodData: Composable analytics platform with headless BI and embedded dashboards.

How Pocket entities map to GoodData

Pocket entityGoodData objectNotes
recordspocket_recordsid PK · custom fields → flattened columns
eventspocket_eventsdate dimensions events
configuration objectspocket_configuration_objectsid PK · linked to pocket_records

FAQ

How does Datrise handle Pocket's custom fields in GoodData?

Flexible values are stored as flattened columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native GoodData types.

How does the Pocket to GoodData sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

Early access

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